1. Field of the Invention
The present invention relates to a gain adjusting device for a PID controller for controlling the rotational speed of an internal combustion engine, for setting a gain with respect to the PID controller for controlling the rotational speed of the internal combustion engine.
2. Description of the Related Art
In a PID controller which is a type of feedback controller, a PID controller is known in which an amount of gain adjustment is set by making use of fuzzy inference so as to effect gain adjustment automatically (refer to Japanese Patent Application Laid-Open Nos. 241006/1987 and 258003/1989, for example). In these PID controllers making use of fuzzy inference, a rule base consisting of a multiplicity of rules stating the human knowhow for gain adjustment is stored in advance.
A plurality of characteristic variables for evaluating the controlled state are set in an object to be controlled by the PID controller. The multiplicity of rules are prepared by using the knowhow employed by human beings in gain adjustment. Specifically, the rules are stated in the form of compound propositions of an IF-THEN format in which all the characteristic variables to be evaluated are connected together. For instance, in a case where a settling time, an amount of overshoot, and the damping of oscillations are set as the characteristic variables, the following compound proposition is used: "If the setting time =slow, overshoot amount =large, and the damping of oscillations =bad, then increase the value of proportional (P) gain, increase the value of integral (I) gain, and increase the value of differential (D) gain." In addition, the PID controller stores in the form of membership functions antecedents for determining the goodness of fit with respect to the respective rules by evaluating the detected characteristic variables, as illustrated in FIGS. 14A to 14D. In addition, the PID controller stores in the form of membership functions consequents for weighting the amounts of gain adjustment indicated by the respective rules in correspondence with the goodness of fit concerning the respective rules, as illustrated in FIGS. 15A to 15C.
When amounts of gain adjustment are actually set by making use of fuzzy inference, the plurality of characteristic variables are detected, and the goodness of fit concerning the respective rules on the detected characteristic variables is determined by using the membership functions of the antecedents, as shown in FIG. 16. Then, in the membership functions of the consequents, graphic forms, in which apex portions of the triangles corresponding to the amounts of gain adjustment indicated by the rules are cut off by the goodness of fit concerning the respective rules, are obtained for each rule. The graphic forms obtained for the respective rules are superposed one on top of another, and the center of gravity is determined, so as to set an amount of gain adjustment. This processing corresponds to processing for calculating an average value by weighting the amounts of gain adjustment indicated by the rules in correspondence with the goodness of fit of the rules. The amount of gain adjustment set in the above-described processing reflects the knowhow used by human beings in gain adjustment, it is possible to effect appropriate gain adjustment in agreement with the human thinking in comparison with the PID gain adjusting device which does not adopt fuzzy inference.
However, the setting of the amount of gain adjustment in the above-described PID controller is effected by using the rule base stating the rules in the form of compound propositions in which all the characteristic variables for evaluation are connected together, as described above. Therefore, if it is assumed that the number of divisions of evaluation in the antecedent (i.e., the number of items of evaluation, such as "slightly large" and "small," with respect to a single characteristic variable) is m, and the number of characteristic variables to be evaluated is n, then since there are m divisions for each characteristic variable, the number of combinations of the rules reaches a gigantic number of m raised to the n th power. For instance, in a case where the number of divisions is 4, and the number of characteristic variables is 5, the number of combinations of the rules becomes 1000 or more.
If an attempt is made to increase the number of requirements respect to the controlled state, it is necessary to evaluate the controlled state by increasing the numbers of divisions and characteristic variables. However, if the numbers of divisions and characteristic variables are increased as mentioned above, according to the method of setting the amount of gain adjustment described above, the number of combinations of the rules reaches an enormous number, thereby rendering the structuring per se of the rules difficult. For this reason, it has been impossible to apply the PID gain adjusting device using a conventional fuzzy inference method to control of the rotational speed of an internal combustion engine for which a multiplicity of items of evaluation need to be set.
In addition, in the internal combustion engines, the rotation becomes unstable particularly in a low rotational speed range in which an output shaft rotates at a relatively low speed, and it becomes impossible to control each of the characteristic variables within a permissible range. However, if an attempt is made to control the rotational speed of the internal combustion engine according to the conventional method of setting the amount of gain adjustment, the gain is changed toward a high-gain side in an attempt to control fluctuations in rotational speed in the low rotational speed range, which increases the magnitude of the oscillations to the contrary, and an attempt is subsequently made to control these oscillations by lowering the gain. This disadvantageously creates a situation in which the gain cannot be made to converge.